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-consuming and often fail to express target proteins at sufficient levels. In this project, you will investigate whether mammalian cell lines can be genetically altered to enhance expression of challenging
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Status: Open Applications open: 23/06/2025 Applications accepted at any time View printable version [.pdf] About this scholarship Description/Applicant information This PhD scholarship supports a student passionate about developing novel therapeutics for brain diseases such as stroke and...
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indicate that you are applying for a 'Faculty of Science Targeted Scholarship' at question two in the financial details section of the application form have a commencement date no later than 30 September
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the student to guide the research, but preliminary ideas include: Exploring whether suggested discharge limits derived from single organism experiments are protective of microbial communities; Using Bayesian
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Methods Monitoring biodiversity at scale is vital for meeting global conservation targets, yet manual field surveys are too expensive and time consuming. Automated systems like camera traps now generate
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strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material
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these changes affect ecosystem functions. To extend these analyses to new types of data and questions, we develop state-of-the-art hierarchical Bayesian methodology. We also actively apply our research to more
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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised